English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 41 lectures (3h 20m) | 2.08 GB
Master Speech Recognition with Python: From Fundamentals to Cutting-Edge AI Applications
Take the Speech Recognition with Python course and step into the fascinating world of Speech Recognition. Gain the skills to transform spoken language into actionable insights – a crucial skill in the age of AI. This course is your gateway to mastering the technology behind virtual assistants, voice-activated systems, and automated transcription tools. Whether you’re an aspiring AI engineer, data scientist, AI developer, or a professional looking to enhance their technical skill set, this course equips you with everything you need to excel in the speech recognition domain.
What Will You Learn?
- The Foundations of Speech Recognition: Explore how audio is transformed into digital data, processed, and converted into text. Build a strong theoretical base, from acoustic modeling to advanced algorithms.
- Hands-On Python Projects: Use Python’s robust libraries to process, visualize, and transcribe audio files. Learn both online and offline approaches for developing speech-to-text applications.
- Cutting-Edge Techniques: Dive into Hidden Markov Models, Neural Networks, and Transformers. Understand the mechanics behind modern speech recognition systems and discover how they power real-world applications.
- Practical Applications: Master the skills to build voice-activated assistants, enhance accessibility, and develop solutions for data-driven decision-making.
Table of Contents
Introduction
1 Welcome to the World of Speech Recognition
2 Course Approach
3 How It All Started Formants, Harmonics, and Phonemes
4 Development and Evolution
Sound and Speech Basics
5 How Do Humans Recognize Speech
6 Fundamentals of Sound and Sound Waves
7 Properties of Sound Waves
Analog to Digital Conversion
8 Key Concepts Sample Rate, Bit Depth, and Bit Rate
9 Audio Signal Processing for Machine Learning and AI
Audio Feature Extraction for AI Applications
10 Time-Domain Audio Features
11 Frequency-Domain and Time-Frequency-Domain Audio Features
12 Time-Domain Feature Extraction Framing and Feature Computation
13 Frequency-Domain Feature Extraction Fourier Transform
Speech Recognition Mechanics
14 Acoustic and Language Modeling
15 Hidden Markov Models (HMMs) and Traditional Neural Networks
16 Deep Learning Models CNNs, RNNs, and LSTMs
17 Advanced Speech Recognition Systems Transformers
18 Building a Speech Recognition Model Part I
19 Building a Speech Recognition Model Part II
20 Selecting the Appropriate Speech Recognition Tool
21 Expanding Beyond the Tools We’ve Covered
Setting Up the Environment
22 Installing Anaconda
23 Setting Up a New Environment
24 Installing Packages for Speech Recognition
25 Importing The Relevant Packages in Jupyter
Transcribing Audio with Google Web Speech API
26 Audio File Formats for Speech Recognition
27 Importing Audio Files in Jupyter Notebook
28 The SpeechRecognition Library Google Web Speech API
29 Evaluation Metrics WER and CER
30 Calculating WER and CER in Python
Background Noise and Spectrograms
31 Understanding Noise in Audio Files
32 Creating a Spectrogram with Python
33 Dealing with Background Noise
Transcribing Audio with OpenAI’s Whisper
34 Whisper AI Transformer-based Speech-to-Text
35 Homework Assignment
36 Transcribing Multiple Audio Files from a Directory
37 Saving Audio Transcriptions to CSV for Easy Analysis
38 Reversing the Process AI-Powered Text-to-Speech
Final Discussion and Future Directions
39 Modern Practices and Applications
40 Challenges and Limitations
41 The Future of Speech Recognition with AI
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